Methods and apparatus to estimate demography based on aerial images

ABSTRACT

Methods and apparatus to estimate demography based on aerial images are disclosed. An example method includes analyzing a first aerial image of a first geographic area to detect a first plurality of objects, and estimating a demographic characteristic of the first geographic area based on the first plurality of objects.

FIELD OF THE DISCLOSURE

This disclosure relates generally to surveying and, more particularly,to methods and apparatus to estimate demography based on aerial images.

BACKGROUND

Manufacturers of goods sometimes wish to determine where new markets areemerging and/or developing. Smaller, growing markets are often desirabletargets for such studies. As these markets grow larger and/or mature,previous market research becomes obsolete and may be updated and/orperformed again.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system to estimate demographybased on aerial images.

FIG. 2 illustrates an example aerial image of a first example geographicarea in which the system of FIG. 1 may detect a relatively high amountof green space.

FIG. 3 illustrates an example aerial image of a second examplegeographic area in which the system of FIG. 1 may detect a relativelyhigher building density than the first geographic area of FIG. 2.

FIG. 4 illustrates an example aerial image of a third example geographicarea in which the system of FIG. 1 may detect a number of swimmingpools.

FIG. 5 illustrates an example aerial image of a fourth examplegeographic area in which the system of FIG. 1 may detect buildingshaving a first roof type.

FIG. 6 illustrates an example aerial image of a fifth example geographicarea 600 in which the system of FIG. 1 may analyze traffic information.

FIG. 7 is a flowchart representative of example machine readableinstructions which, when executed, cause a processor to implement theexample system of FIG. 1 to estimate a demography of a geographic areaof interest.

FIGS. 8A and 8B collectively illustrate a flowchart representative ofexample machine readable instructions which, when executed, cause aprocessor to implement the example object classifier of FIG. 1 toclassify an object detected in an aerial image.

FIG. 9 is a flowchart representative of example machine readableinstructions which, when executed, cause a processor to implement theexample demography database of FIG. 1 to identify geographic areassimilar to the geographic area of interest.

FIG. 10 is a flowchart representative of example machine readableinstructions which, when executed, cause a processor to implement theexample search analyzer, the example search traffic collector, and/orthe example search library to analyze search information to estimatedemographic characteristic(s) of a geographic area.

FIG. 11 is a flowchart representative of example machine readableinstructions which, when executed, cause a processor to implement theexample driving traffic analyzer and/or the example driving trafficcollector of FIG. 1 to analyze driving information to estimatedemographic characteristic(s) of a geographic area.

FIG. 12 is a block diagram of an example processor platform capable ofexecuting the instructions of FIGS. 7, 8A, 8B, 9, 10, and/or 11 toimplement the system of FIG. 1.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Traditional methods for determining demography (e.g., household income,population density, population age, family size, etc.) of a geographicarea of interest employ human surveyors. Such traditional methods sufferfrom many shortcomings including high costs, low temporal resolution,and/or an inability to determine demography in many areas due todangerous conditions and/or geopolitical factors. Determining orestimating demography using known methods is sometimes impracticable orimpossible in the dynamic economies of the developing world. Examplemethods and apparatus disclosed herein employ aerial images and dataderived from actual knowledge of geographic areas (“ground truth data,”such as from manually sampling an area) to inform estimates of areasthat are not sampled directly by human surveyors. Example methods andapparatus disclosed herein reduce costs associated with determiningdemography using known methods and provide a means to estimatedemographics in areas that are difficult to sample with human surveyorsand/or cannot be sampled directly.

Example methods and apparatus disclosed herein enable improved estimatesof demography in locations where human sampling was previouslyimpracticable. For example, some locations may previously have requiredthe assumption of excessive human and/or monetary risk to obtainuncertain demography information. Some example methods and apparatusdisclosed herein may decrease the uncertainty of obtaining demographyinformation and/or decrease the monetary and/or human time investmentsrequired to obtain the demography information, thereby lowering risk andincreasing the practicability of performing sampling.

Example methods and apparatus disclosed herein obtain an aerial image ofa geographic area for which demography is to be estimated and analyzesthe aerial image to detect objects. Some such example methods andapparatus classify the objects and/or the aerial image as a whole todetermine whether the objects are representative of objects or featuresassociated with types of demographic characteristics. For example, areasindicating a higher average household income may include a number ofswimming pools, have roofs of buildings that are a certain color (e.g.,copper-colored in some areas, may be any color for a given area), and/orsubstantial green or open/undeveloped space (e.g., space on whichbuildings are not present, but may not necessarily be green in color).In some examples, average household income may be estimated based ondistance(s) from the geographic area to a landmark or other referencelocation, based on the characteristics of local transportation resources(e.g., widths of nearby roadways, distance to nearby publictransportation systems, etc.), and/or based on a density of buildings inthe geographic area.

Example methods and apparatus disclosed herein determine whether objectsare representative of demographic characteristics by comparing theaerial image of the geographic area of interest to aerial image(s) ofother geographic areas for which ground truth data has been obtained(e.g., ground truth data corresponding to areas that have been manuallysampled or surveyed). Based on the comparison, example methods andapparatus estimate the demography of the geographic area of interest.Example methods and apparatus identify similar geographic areas bygenerating a signature of the geographic area of interest and comparingthe signature to reference signatures of aerial images of the geographicareas for which ground truth data has been obtained. In some examples,the signature is generated via a hash, such as a perceptual hash. Insome examples, the hash may be modified to focus on the presence and/orabsence of identified objects.

Example methods and apparatus disclosed herein combine aerialimage-based demography estimation with search information and/or drivingtraffic information to more accurately estimate demography of ageographic area of interest. In some examples, searches (e.g., Internetsearches through web portals such as Google or Bing) that originate froma geographic area of interest are analyzed to determine whether thetopics being searched are indicative of demography. For example, thetypes and/or quantities of search topics in a geographic area ofinterest may indicate a demographic characteristic and a relevantprevalence of the characteristic in the geographic area. For example,searches pertaining to items used by children may indicate the presenceof people of child-rearing ages, and the quantities of such searchesindicate a number of persons of that age in the geographic area.

Example methods and apparatus disclosed herein may further collectdriving traffic information (e.g., cars, buses, trucks, etc.) anddetermine demography and/or demography changes based on the drivingtraffic information. For example, higher traffic in particular areas mayindicate a higher number of cars (or other vehicles) being present in aparticular area or passing through the area from another area. A highernumber of vehicles (e.g., cars, trucks, motorcycles, etc.) may indicate,for example, a higher relative income level.

FIG. 1 is a block diagram of an example system 100 constructed inaccordance with the teachings of this disclosure to estimate demographybased on aerial images. The example system 100 of FIG. 1 includes anobject detector 102, an object classifier 104, a density calculator 106,and a demography estimator 108. The example system 100 of FIG. 1 usesaerial images of a geographic area of interest 110 (e.g., an area forwhich the demography is to be estimated), aerial images of othergeographic areas, and sampling or ground truth data of the othergeographic areas to estimate the demography of the geographic area ofinterest 110.

The example system 100 of FIG. 1 further includes an aerial imagerepository 112 that provides image(s) of the specified geographic areaof interest 110 to a requester (e.g., via a network 114 such as theInternet). The example images may include aerially-generated images(e.g., images captured from an aircraft) and/or satellite-generatedimages. The images may have any of multiple sizes and/or resolutions(e.g., images captured from various heights over the geographic areas).Example satellite and/or aerial image repositories that may be employedto implement the example aerial image repository 112 of FIG. 1 areavailable from DigitalGlobe®, GeoEye®, RapidEye, Spot Image®, and/or theU.S. National Aerial Photography Program (NAPP). The example aerialimage repository 112 of the illustrated example may additionally oralternatively include geographic data such as digital maprepresentations, source(s) of population information, building and/orother man-made object information, and/or external source(s) for parks,road classification, bodies of water, etc.

The example system 100 of FIG. 1 further includes an example demographydatabase 116. The example demography database 116 of FIG. 1 storesinformation about the demographies of geographic areas. The demographicinformation stored in the demography database 116 of the illustratedexample includes government and/or private census data, survey and/orsampling data, geographic data, and/or any other type of demographyinformation. The example demography database 116 of FIG. 1 stores thedemographic information in association with geographic information(e.g., global positioning system (GPS) coordinates) corresponding to thedemographic information. The example demography database 116 of FIG. 1may further store a count and/or estimation of objects detectable in theaerial image of the geographic locations for which demographicinformation is known.

The example demography database 116 of FIG. 1 responds to a request fordemographic information for an area defined by coordinates by returningthe requested demographic information. In some examples, the demographydatabase 116 scales, extrapolates, and/or otherwise adjusts demographicinformation for requested areas that do not precisely correspond to thegeographic information stored in the demography database 116.

The example object detector 102 of FIG. 1 obtains (e.g., requests andreceives, accesses from storage, etc.) an aerial image of the geographicarea of interest 110 from the aerial image repository (e.g., via thenetwork 114). Using the aerial image, the example object detector 102detects objects in the geographic area. The example object detector 102of FIG. 1 may use color analysis, edge detection, and/or any othersuitable automatic image analysis technique and/or object definitions toidentify objects. Example objects include roofs, green spaces, swimmingpools, landmarks, and/or roadways. The example object detector 102 ofthe illustrated example further detects characteristics of the examplegeographic area based on the aerial image. For example, the objectdetector 102 of the illustrated example determines a distance from thegeographic area (e.g., a particular position in the geographic area) toa landmark or other reference location, which may or may not be withinthe aerial image.

The example object classifier 104 of FIG. 1 classifies the objectsdetected by the object detector 102. The object classifier 104 of theillustrate example classifies objects by analyzing locations in theaerial image corresponding to the objects detected by the objectdetector 102. Example objects that may be detected by the objectclassifier 104 include swimming pools, structure roofs of a designatedtype, green spaces, landmarks, distances to one or more referencelocation(s), or a roadway having a width within a designated range. Theexample object classifier 104 of FIG. 1 further determines a density ofbuildings in the geographic area based on the aerial image.

To detect whether an object is a swimming pool, the example objectclassifier 104 of FIG. 1 determines whether the color of the object inthe aerial image is within a color range (e.g., dark blue to light blue)and whether the area of the object is less than an upper threshold(e.g., to avoid classifying larger bodies of water as swimming pools).In some examples, the object classifier 104 further determines whetherthe color of the object is greater than a lower threshold.

To detect whether an object is a green space, the object classifier 104of the illustrated example determines whether the color of the objectcorresponds to that of local green space. While some green space may bein the green color range, other green space may be in other color ranges(e.g., a brown or red color range). In some examples, the objectclassifier 104 determines whether the object is at least a thresholdsize (e.g., to discount negligible green spaces). In some otherexamples, the object classifier 104 does not filter green spaces basedon size when all green spaces are considered to be representative ofdemographic characteristics.

To detect whether the object is a roof of a designated color, the objectclassifier 104 of the illustrated example determines whether the colorof the object is within a color range of the roof type of interest. Thecolor of a roof type may be different for different geographic areas andmay indicate different income ranges (e.g., lower than average, average,higher than average, etc.). For example, in some areas a copper-coloredroof indicates a higher average income, while in other areas awhite-colored or black-colored roof may indicate a higher averageincome. The example color range(s) may include colors based on shadowsof the color range(s) of interest. In some examples, the objectclassifier 104 determines whether the object has one of a number ofshapes consistent with that of a building of interest and/or whether theobject is less than an upper threshold size to increase the likelihoodthat the identified object is a roof.

To detect whether the object is a roadway of a particular width, theobject classifier 104 of the illustrated example determines a scale ofthe aerial image to the geographic area (e.g., 50 meters per inch, 1meter per pixel, etc.). The example object classifier 104 of theillustrated example converts the width of the roadway into actualdistance.

The example object detector 102 of FIG. 1 determines a signature of theaerial image (e.g., a portion of the aerial image corresponding to thegeographic area of interest 110). To this end, the example objectdetector 102 includes a signature generator 118, which generates thesignature of the aerial image. Generating the signature may includecropping or limiting the aerial image to the portion of the aerial imagethat corresponds to the geographic area of interest 110. In someexamples, the signature generator 118 performs a hash of the aerialimage, such as a perceptual hash. In some other examples, the signaturegenerator 118 generates a signature based on the detected and/orclassified objects.

The example demography estimator 108 of FIG. 1 receives theclassifications (e.g., a swimming pool, a first type of structure roof,a green space, a distance range to a reference location, or a roadwaywidth range) of the objects from the object classifier 104 and asignature of the aerial image from the signature generator 118. Theexample demography estimator 108 of FIG. 1 communicates with thedemography database 116 to request geographic areas and correspondingdemographies of geographic areas that are similar to the geographic areaof interest 110 based on the number(s) and/or classification(s) of theobjects in the aerial image of the geographic area of interest 110. Forexample, the demography estimator 108 may query the demography database116 using the number(s) and/or classification(s) of the objects in theaerial image and providing a threshold similarity to the objects. Theexample demography database 116 returns to the demography estimator 108any geographic areas that are within the threshold similarity to thegeographic area of interest 110 based on the numbers and/orclassifications of the objects, and the demographies corresponding tothe returned geographic areas.

Additionally or alternatively, the example demography estimator 108 ofFIG. 1 communicates with the demographic database 116 to requestgeographic areas and corresponding demographies that are similar to thegeographic area of interest 110 based on the signature of the aerialimage and based on signatures stored in the example demography database116. For example, the demography estimator 108 may query the demographydatabase 116 using the signature of the aerial image as a key andproviding a threshold similarity to the signature. The exampledemography database 116 returns to the demography estimator 108 anygeographic areas that are within the threshold similarity to thegeographic area of interest 110 based on the signatures, and thedemographies corresponding to the returned geographic areas.

Based on the classifications of the objects in the aerial image, thedensity of objects in the aerial image, and/or the signature of theaerial image, and based on the demographies received from the demographydatabase 116, the example demography estimator 108 of FIG. 1 estimatesthe demography of the geographic area of interest. For example, thedemography estimator 108 of the illustrated example weights and/orcombines the demographies received from the demography database 116based on relative similarities of the geographic areas returned by thedemography database 116 (e.g., determined based on objectclassifications and/or signatures) to the geographic area of interest110. In the example of FIG. 1, the demography estimator 108 gives higherweights to the demographies of areas that are more similar to thegeographic area of interest 110 and lower weights to the demographies ofareas that are less similar. The example demography estimator 108 mayfurther weight the demographies of the areas based on relative sizes ofthe geographic areas to the geographic area of interest 110.

The example demography estimator 108 of the illustrated exampleestimates the demography based on the weighted and/or scaleddemographies by, for example, averaging the weighted and/or scaleddemographies (e.g., root-mean-squared averaging, determining the mean,etc.). The example demography estimator 108 of the illustrated examplethen outputs the estimated demography and/or stores the estimateddemography in the demography database 116 in association with adefinition of the geographic area of interest 110, with a timestamp,with the aerial image of the geographic area of interest 110, with thesignature of the aerial image, and/or with the classifications of theobjects in the aerial image.

The example system 100 of FIG. 1 may repeat estimation of the demographyof the geographic area of interest 110 at different times (e.g.,quarterly, bi-annually, annually, etc.) to monitor the development ofthe geographic area of interest 110 over time and/or to predict futuredevelopment. The demography may be used to, for example, identifyemerging markets and/or a developing potential for marketing goods andservices to an underserved demographic.

The example system 100 of FIG. 1 further includes a search analyzer 120to determine demography information based on searches (e.g., Internetsearches) performed from the geographic area of interest. The examplesearch analyzer 120 includes a search traffic collector 122. The examplesearch traffic collector 122 of FIG. 1 collects search informationoccurring from locations (e.g., IP addresses) within the geographic areaof interest 110. For example, the search traffic collector 122 of FIG. 1receives search information (e.g., search terms for searches performed)from search providers, such as Google, Bing, Yahoo!, and/or any othersearch or other Internet providers (e.g., Amazon.com, etc.) of interest.To obtain the search information, the search traffic collector 122determines IP addresses corresponding to the geographic area of interest(e.g., from Internet service providers serving the geographic area ofinterest), and requests searches corresponding to the IP addresses. Inother examples, the entity conducting the study recruits panelists toparticipate in the study and downloads online meters to the panelists'computers or other Internet access devices to automatically collect dataindicative of interests (e.g., URLs, search terms, etc.).

Irrespective of how the data is collected, upon receiving the searchinformation, the example search analyzer 120 of FIG. 1 determinesdemographic characteristics of the area based on a qualitative analysisof the search topics or terms. For example, the search analyzer 120 maycompare the search topics to a search library 124 that correlates searchtopics to demographic characteristics. The example search analyzer 120may further determine a number of each type of search or class of search(e.g., searches having particular keyword(s)) to determine a prevalenceof demographic characteristics in the geographic area of interest 110.For example, a higher number of a particular search topic may beindicative of a higher number of persons having a correspondingdemographic characteristic in the geographic area of interest 110.

The example search analyzer 120 of FIG. 1 additionally or alternativelyobtains a comparison of demographic characteristics to search topicsfrom the example demography database 116. The example search analyzer120 may compare types and/or quantities of search topics from thegeographic area of interest 110 to types and/or quantities of searchtopics stored in the demography database 116. Based on the comparisonsand the demographic characteristics received from the demographydatabase 116 in association with the search topics, the example searchanalyzer 120 of FIG. 1 determines corresponding estimated demographiccharacteristics for the geographic area of interest. The example searchanalyzer 120 and/or the example driving traffic analyzer 126 provide theestimated demography information to the example demography estimator108, which combines the search-based demography estimates, traffic-baseddemography estimates, and/or aerial image-based estimates of demographyto determine an overall estimate of demography.

The example driving traffic analyzer 126 of FIG. 1 analyzes drivingtraffic in the geographic area of interest 110 to estimate demographyinformation. The example driving traffic analyzer 126 includes a drivingtraffic collector to obtain driving traffic information for thegeographic area of interest. Example sources of driving trafficinformation include publicly available traffic databases such as GoogleMaps. Example traffic information shows color-coded overlays on a map ofa roadway to indicate relative speeds of traffic over the correspondingportion of the roadway. In some examples, the driving traffic collector128 obtains traffic information for areas outside of the geographic areaof interest 110 (e.g., because many persons having an abode within thegeographic area of interest 110 may commute or otherwise travel toplaces outside of the geographic area of interest 110).

The example traffic analyzer 126 of FIG. 1 analyzes the trafficinformation (e.g., interprets the color-coded overlay) and converts thetraffic information to a number of cars (and/or other vehicles such astrucks and/or motorcycles, etc.) associated with the example geographicarea of interest. The example traffic analyzer 126 further identifiesflows of traffic (e.g., based on times of day) to identify the sourcesof the traffic and/or a proportion of the traffic that originates in thegeographic area of interest. The example traffic analyzer 126 combinesthe traffic originating in the geographic area of interest 110 withtraffic arriving at a destination (e.g., a downtown area, an industrialarea, etc.) and characteristics of persons associated with thedestinations (e.g., higher income, lower income, etc.) to estimate oneor more characteristics of the geographic area of interest 110.Similarly, the example traffic analyzer 126 may determine an amount oftraffic terminating in the geographic area of interest 110 to determine,for example, a level of commercial, retail, and/or industrial activityoccurring within the geographic area of interest 110.

The example driving traffic analyzer 126 provides the driving trafficinformation to the example demography estimator 108. The exampledemography estimator 108 considers the traffic information whenestimating the demography of the example geographic area of interest110.

In some examples, the driving traffic analyzer 126 analyzes the aerialimage of the geographic area of interest 110 to identify one or moreparking lots (e.g., a space having a black or other color and having anumber of car-type objects) and/or roadside parking areas (e.g., spaceshaving a number of car-type objects in a line adjacent a roadway). Theexample driving traffic analyzer 126 may combine the traffic information(e.g., originating and/or terminating traffic information) with theparking lot data to further estimate the number of cars in thegeographic area of interest 110.

FIG. 2 illustrates an example aerial image 200 of a first examplegeographic area 202 in which the system 100 of FIG. 1 may detect arelatively high amount of green space. The image 200 of FIG. 2 may beprocessed by the example object detector 102, the example objectclassifier 104, the example density calculator 106, and/or the examplesignature generator 118 of FIG. 1.

As illustrated in FIG. 2, the example geographic area 202 includesmultiple buildings 204 generally intermixed with green space 206(illustrated by cross-hatching in FIG. 2). The example object detector102 of FIG. 1 may detect the buildings 204 and the green space 206 basedon, for example, the shapes of the buildings 204 and the color of theaerial image representing the green space 206. The example objectclassifier 104 of FIG. 1 classifies the example buildings 204 and/ordetermines whether the buildings have a particular color of roof. Theexample density calculator 106 of FIG. 1 calculates a density of thebuildings 204 (e.g., a number of buildings 204 and/or an area occupiedby the buildings per unit area) and/or calculates a density of the greenspace 206 (e.g., a number of distinct green spaces separated bynon-green spaces and/or an amount of green space per unit area).

FIG. 3 illustrates an example aerial image 300 of a second examplegeographic area 302 in which the system of FIG. 1 may detect arelatively higher building density than the first geographic area 202 ofFIG. 2. The example geographic area 302 of FIG. 3 includes a number ofbuildings 304. The example density calculator 106 of FIG. 1 maycalculate the density of the buildings 304 in the geographic area 302,which is higher than the density of the buildings 204 in the examplegeographic area 202 of FIG. 2. The density may be based on a number ofobjects detected and/or based on an area occupied by the objectsrelative to the size of the geographic area 202.

FIG. 4 illustrates an example aerial image 400 of a third examplegeographic area 402 in which the example system 100 of FIG. 1 may detecta number of swimming pools 404. The example object detector 102 and theexample object classifier 104 of FIG. 1 may identify and classify theswimming pools 404 based on the color of the aerial image in thelocations of the swimming pools 404 and the sizes of the swimming pools404. The example object classifier 104 of FIG. 1 classifies the swimmingpool 404 a as a swimming pool (e.g., as a public swimming pool) based onthe size (e.g., a size falling between lower and upper thresholds), butdoes not classify a body of water 406 as a swimming pool due to the sizeof the body of water 406 (e.g., the water 406 has a size greater thanthe upper threshold). While example swimming pools 404 and bodies ofwater 406 are illustrated in FIG. 4, the threshold size between a bodyof water and a swimming pool may be different than an example thresholdused by the example object classifier 104 of FIG. 1 to distinguish theexample swimming pools 404 and the example bodies of water 406 of FIG.4. For example, private and/or public swimming pools may be sizeddifferently in different geographic areas. The example object classifier104 of FIG. 1 may further determine whether objects are swimming poolsbased on the presence and/or absence of straight lines. For example,larger public swimming pools may be defined by one or more straightlines. However, shorelines of large bodies of water may also appear tobe a straight line. Accordingly, the example object classifier 104 ofFIG. 1 performs the classification of swimming pools based on acombination of straight lines, shapes (e.g., kidney shaped pools),and/or sizes.

FIG. 5 illustrates an example aerial image 500 of a fourth examplegeographic area in which the system of FIG. 1 may detect buildings 502having a first roof type. The example object classifier 104 of FIG. 1classifies the example buildings 504 by determining a color of thebuildings 504 and comparing the color to a color range. The exampleobject classifier 104 may further compare a size of the buildings 504 toan upper threshold and/or to a lower threshold. In some examples, theobject classifier 104 determines whether the shape of the buildings 504are typical of buildings of interest (e.g., square, rectangular, and/orpolygonal objects are consistent with housing units, while circular,oval, or irregularly shaped objects are not consistent with housingunits in some areas).

FIG. 6 illustrates an example aerial image of a fifth example geographicarea 600 in which the system of FIG. 1 may analyze traffic information.The example aerial image of FIG. 6 includes an overlay on whichdifferent traffic speeds are illustrated. A first illustrated trafficindicator 602 illustrates that a first driving traffic flow along afirst path is traveling at a first (e.g., faster) speed range. A secondillustrated traffic indicator 604 illustrates that a second drivingtraffic flow along the first path, in the opposite direction, istraveling at a second (e.g., slower speed range). The example trafficanalyzer 126 of FIG. 1 may analyze the traffic indicators 602, 604, thebeginning and ending points of the traffic indicators 602, 604, and/orany indications of parking lots (e.g., a parking lot 606) to identifythat the traffic associated with the indicator 602 originates in thegeographic area 600, and the traffic associated with the indicator 604terminates in the geographic area 600.

While an example manner of implementing the system 100 is illustrated inFIG. 1, one or more of the elements, processes and/or devicesillustrated in FIG. 1 may be combined, divided, re-arranged, omitted,eliminated and/or implemented in any other way. Further, the exampleobject detector 102, the example object classifier 104, the exampledensity calculator 106, the example demography estimator 108, theexample aerial image repository 112, the example demography database116, the example signature generator 118 and/or, more generally, theexample system 100 of FIG. 1 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example object detector 102, the exampleobject classifier 104, the example density calculator 106, the exampledemography estimator 108, the example aerial image repository 112, theexample demography database 116, the example signature generator 118and/or, more generally, the example system 100 could be implemented byone or more analog or digital circuit(s), logic circuits, programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example object detector 102, theexample object classifier 104, the example density calculator 106, theexample demography estimator 108, the example aerial image repository112, the example demography database 116, and/or the example signaturegenerator 118 is/are hereby expressly defined to include a tangiblecomputer readable storage device or storage disk such as a memory, adigital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc.storing the software and/or firmware. Further still, the example system100 of FIG. 1 may include one or more elements, processes and/or devicesin addition to, or instead of, those illustrated in FIG. 1, and/or mayinclude more than one of any or all of the illustrated elements,processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the system 100 of FIG. 1 are shown in FIGS. 7, 8A, 8B, 9,10, and/or 11. In this example, the machine readable instructionscomprise programs for execution by a processor such as the processor1212 shown in the example processor platform 1200 discussed below inconnection with FIG. 12. The program may be embodied in software storedon a tangible computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 1212, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 1212 and/or embodied in firmware or dedicatedhardware. Further, although the example programs are described withreference to the flowcharts illustrated in FIGS. 7, 8A, 8B, 9, 10,and/or 11, many other methods of implementing the example system 100 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined.

As mentioned above, the example processes of FIGS. 7, 8A, 8B, 9, 10,and/or 11 may be implemented using coded instructions (e.g., computerand/or machine readable instructions) stored on a tangible computerreadable storage medium such as a hard disk drive, a flash memory, aread-only memory (ROM), a compact disk (CD), a digital versatile disk(DVD), a cache, a random-access memory (RAM) and/or any other storagedevice or storage disk in which information is stored for any duration(e.g., for extended time periods, permanently, for brief instances, fortemporarily buffering, and/or for caching of the information). As usedherein, the term tangible computer readable storage medium is expresslydefined to include any type of computer readable storage device and/orstorage disk and to exclude propagating signals. As used herein,“tangible computer readable storage medium” and “tangible machinereadable storage medium” are used interchangeably. Additionally oralternatively, the example processes of FIGS. 7, 8A, 8B, 9, 10, and/or11 may be implemented using coded instructions (e.g., computer and/ormachine readable instructions) stored on a non-transitory computerand/or machine readable medium such as a hard disk drive, a flashmemory, a read-only memory, a compact disk, a digital versatile disk, acache, a random-access memory and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm non-transitory computer readable medium is expressly defined toinclude any type of computer readable device or disk and to excludepropagating signals. As used herein, when the phrase “at least” is usedas the transition term in a preamble of a claim, it is open-ended in thesame manner as the term “comprising” is open ended.

FIG. 7 is a flowchart representative of example machine readableinstructions 700 which, when executed, cause a processor to implementthe example system 100 of FIG. 1 to estimate a demography of ageographic area of interest. The example instructions 700 of FIG. 7 maybe executed to implement the example object detector 102, the exampleobject classifier 104, the example density calculator 106, the exampledensity estimator 108, the example aerial image repository 112, and theexample demography database 116.

The example object detector 102 obtains (e.g., receives, accesses fromstorage) an aerial image of a geographic area of interest 110 (block702). For example, the object detector 102 may request and receive theaerial image from the example aerial image repository 112 of FIG. 1. Theexample object detector 102 and/or the example density calculator 106 ofFIG. 1 analyzes the aerial image (e.g., using image analysis techniques)to detect objects and/or determine characteristics of the geographicarea (block 704). For example, the object detector 102 may detectobjects and/or determine characteristics based on the aerial image basedon shapes, colors, and/or boundaries in the image. Additionally oralternatively, the example density calculator 106 may determine adensity of one or more objects in the aerial image.

The example object classifier 104 of FIG. 1 selects a detected object(block 706) and classifies the selected object (block 708). For example,the object classifier 104 may determine a type of the object based oncharacteristics of the object. In some examples, one or more of theobjects may not be classified or may be classified as an unknown typeif, for example, the object is not classified as one of a set ofobjects. Example instructions that may be executed to implement block708 are described below with reference to FIGS. 8A and 8B. The objectclassifier 104 determines whether there are any additional objects to beclassified (block 710). If there are additional objects (block 710),control returns to block 706 to select another object to be classified.

When the detected objects have been classified and there are noadditional objects (block 710), the example demography estimator 108identifies geographic area(s) having aerial images similar to the aerialimage of the geographic area of interest (block 712). For example, thedemography estimator 108 may request geographic areas and/or aerialimages from the example demography database 116 based on a signaturegenerated by the signature generator 118 and/or object classificationsdetermined by the object classifier 104. Example instructions that maybe executed to implement block 712 are illustrated below with referenceto FIG. 9.

The example demography estimator 108 obtains demographic characteristicsof the identified geographic region(s) (block 714). For example, thedemography estimator 108 may receive the demographic characteristics(e.g., population, household income, household population, ages,genders, etc.) of the geographic areas identified by the demographydatabase 116.

The demography estimator 108 estimates the demography of the geographicarea of interest 110 of FIG. 1 based on the demographic characteristicsof the identified geographic area(s) (block 716). For example, thedemography estimator 108 may weight and/or scale the demographiccharacteristics of the identified geographic areas based on respectivesimilarities between the identified geographic areas and the geographicarea of interest 110 (e.g., based on object classifications, numbers,geographic area characteristics such as object density, and/orsignatures of the aerial images). The demographic estimator 108 may thenaverage or otherwise combine the demographic characteristics to estimatethe demographic characteristics of the geographic area of interest 110.In some examples, the example demography estimator 108 estimates thedemography of the geographic area of interest 110 by imputingdemographic characteristics of a second geographic area stored in thedemography database 116 to the example geographic area of interest 110based on similarity between respective signatures and/or objectclassifications. The example instructions 700 of FIG. 7 may then end.

FIGS. 8A and 8B show a flowchart representative of example machinereadable instructions 800 which, when executed, cause a processor toimplement the example object classifier 104 of FIG. 1 to classify anobject detected in an aerial image. The example instructions 800 ofFIGS. 8A and 7B may be executed to implement block 708 of FIG. 7 toclassify a selected object. The example instructions 800 are describedbelow with reference to a selected object (block 706 of FIG. 7).

Referring to FIG. 8A, the example object classifier 104 determineswhether a color of the selected object is equal to a roof color ofinterest (e.g., determined based on color values such as Red-Green-Blue(RGB) encoded values) (block 802), whether the object area is within asize range (e.g., less than an upper threshold and/or greater than alower threshold) (block 804), and whether the selected object has ashape consistent with that of a building of interest (e.g., a house)(block 806). The example roof color of interest may be a color rangeand/or may consider the effects of shadows in the aerial image. Theexample roof size range may be selected to represent adequate buildingsizes to represent the building of interest (e.g., houses, apartmentbuildings, etc.). The acceptable shape(s) may be selected to filternatural objects (e.g., objects not having straight lines) or otherbuilding shapes. If the color of the selected object is a roof color ofinterest (block 802), the selected object area is within a roof sizerange (block 804), and the selected object has a shape consistent withthat of a building of interest (block 806), the example objectclassifier 104 classifies the object as a roof of a first type.

If the object classifier 104 determines that the color of the selectedobject is not a roof color of interest (block 802), that the selectedobject area is too large or too small (block 804), or that the selectedobject has an irregular shape (block 806), the example object classifier104 determines whether the object color is within one or more watercolor range(s) (block 810) whether the object area is within a swimmingpool size range (block 812). The water color range(s) may be selectedbased on the effects of different substances beneath the swimming poolwater, the potential depths of the water, the effects of shadows,unfilled swimming pools, and/or other considerations that may affect theobserved color of swimming pools. The swimming pool size range may beselected based on observed ranges of private and public swimming pools.If the selected object color is within a water color range (block 810),and the selected object area is within a swimming pool size range (block812), the example object classifier 104 classifies the selected objectas a swimming pool (block 814). In some examples, classification of aswimming pool may be based on the presence of straight lines in theobject and/or based on a shape of the object.

Referring to FIG. 8B, if the selected object color is outside of thewater color range(s) (block 810), or the selected object area is notwithin a swimming pool size range (e.g., the object is too small or toolarge) (block 812), the example object classifier 104 determines whetherthe distance from the selected object to the reference object is lessthan a threshold (block 816). If the distance is less than a threshold(block 818), the example object classifier 104 classifies the selectedobject as near reference object (block 818). The example selected objectmay be an object selected by the object detector 102 to determine thedistance from the geographic area of interest 110 to the referenceobject. The example reference object may be a point of interest and/or alandmark, or any other reference object for which a distance from theobject is indicative of at least one demographic characteristic of anarea. In some examples, the object classifier 104 executes blocks 816and 818 in addition to classifying an object as another type of object(e.g., a swimming pool, a roof, a green space, a landmark, etc.).

If the distance is at least a threshold distance (block 816), theexample object classifier determines whether a color of the selectedobject is within a green space color range (block 820) and whether theobject area is greater than a size threshold (block 822). The greenspace color range may be selected to be representative of green spaces(e.g., parks, developed natural areas, green spaces on personal propertysuch as front yards and/or back yards, etc.) for the geographic area ofinterest. For example, a first geographic region may have differentterrain than a second geographic region, necessitating differentvegetation and/or other considerations for local green spaces in therespective regions. The threshold object size for green spaces may be alower threshold to avoid counting negligible green spaces and/or othernon-green space objects that may not be representative of demographiccharacteristics. If the color of the selected object is within the greenspace color range (block 820) and the object area in greater than thethreshold (block 822), the example object classifier 104 classifies theobject as a green space (block 824).

After classifying the object (blocks 808, 814, 818, or 824), or if theobject is not classified (blocks 820, 822), the example instructions 800of FIG. 8B return the classification or lack of classification of theselected object and return control to block 710 of FIG. 7.

FIG. 9 is a flowchart representative of example machine readableinstructions 900 which, when executed, cause a processor to implementthe example system of FIG. 1 to identify geographic areas similar to thegeographic area of interest 110. The example instructions 900 of FIG. 9may be executed by the example demography database 116 of FIG. 1 toimplement block 712 of FIG. 7 to identify geographic area(s) havingaerial images similar to the aerial image of a geographic area ofinterest.

The example demography database 116 obtains (e.g., receives, accessesfrom storage, etc.) a first signature of an aerial image of thegeographic area of interest 110 (block 902). For example, the demographyestimator 108 may receive the example signature (e.g., a hash) from thesignature generator 118 and/or from the example demography estimator 108of FIG. 1.

The example demography database 116 searches a signature library foraerial image(s) having a signature similar to the first signature (block904). For example, the demography estimator 108 may query the demographydatabase 116 using the signature of the first aerial image. The exampledemography database 116 returns similar aerial image(s) based on thesignature and signature(s) of the aerial images stored in the demographydatabase 116. In some examples, the search or query is constrained basedon a geographic region of which the geographic area of interest is aportion, based on a threshold similarity (or difference), and/or basedon areas for which the demography database 116 has associateddemographic information stored.

The example demography database 116 determines geographic area(s)corresponding to the identified aerial image(s) (block 906). Forexample, when the demography database 116 has identified similar aerialimages (block 906), the example demography database 116 determines thegeographic locations corresponding to the aerial images (block 906). Insome examples, the aerial images are mapped to the geographic areas inthe demography database 116. The example instructions 900 may then end,return the selected geographic areas to the demography estimator 108,and return control to block 714 of FIG. 7.

FIG. 10 is a flowchart representative of example machine readableinstructions 1000 which, when executed, cause a processor to implementthe example search analyzer 120, the example search traffic collector122, and/or the example search library 124 to analyze search informationto estimate demographic characteristic(s) of a geographic area.

The example search traffic collector 122 of FIG. 1 obtains (e.g.,receives, accesses from storage, etc.) search traffic originating from ageographic area of interest (block 1002). For example, the searchtraffic collector 122 of FIG. 1 receives search information from searchproviders, such as Google, Bing, Yahoo!, and any other search providersof interest. To obtain the search information, the search trafficcollector 122 determines IP addresses corresponding to the geographicarea of interest (e.g., from Internet service providers serving thegeographic area of interest), and requests searches corresponding to theIP addresses.

The example search analyzer 120 of FIG. 1 identifies search terms fromthe search traffic (block 1004). For example, the search analyzer 120may group searches by keywords. The example search analyzer 120 comparessearch terms to search terms in the example search library 124 todetermine demographic characteristics from the search terms (block1006). The example search analyzer 120 determines a prevalence ofdemographic characteristics based on an amount of search term or typesof search term used (block 1008). For example, the example searchanalyzer 120 may determine a number of each type of search or class ofsearch (e.g., searches having particular keyword(s)) to determine aprevalence of demographic characteristics in the geographic area ofinterest 110. A higher number of a particular search topic may beindicative of a higher number of persons having a correspondingdemographic characteristic in the geographic area of interest 110.

The example instructions 1000 of FIG. 10 then end. The example searchanalyzer 120 of FIG. 1 may provide the estimated demography informationdetermined from the search information to the demography estimator 108of FIG. 1 to be combined with information from aerial images.

FIG. 11 is a flowchart representative of example machine readableinstructions 1100 which, when executed, cause a processor to implementthe example driving traffic analyzer 126 and/or the example drivingtraffic collector 128 of FIG. 1 to analyze driving information toestimate demographic characteristic(s) of a geographic area.

The example driving traffic collector 128 of FIG. 1 obtains drivingtraffic information (block 1102). Example sources of driving trafficinformation include publicly available traffic databases such as GoogleMaps. The example driving traffic analyzer 126 selects a trafficindicator (e.g., the traffic indicators 602, 604 of FIG. 6) (block1104). The example driving traffic analyzer 126 determines a startingpoint, an ending point, and a speed of the flow of the selected trafficindicator (block 1106). For example, a traffic indicator 602, 604 mayinclude one or more routes, one or more traffic speed indicators, and/orany other identifiers. The starting and ending points of the trafficindicator 602, 604 may include changes in traffic speed and/or changesin roadways, for example.

The example driving traffic analyzer 126 determines a number of vehiclesoriginating and/or terminating in the geographic area of interest 110based on the selected traffic indicator (block 1108). For example, thetraffic analyzer 126 may calculate a number of vehicles that enter orexit between adjoining traffic indicators 602, 604 to estimate a numberof vehicles that have originated from a region around the point wherethe adjoining traffic indicators 602, 604 are joined. The exampledriving traffic analyzer 126 and/or the demography estimator 108estimates demography based on the number of vehicles (block 1110). Forexample, the driving traffic analyzer 126 may provide the vehicleinformation for vehicles originating and/or terminating in thegeographic region of interest 110 to the example demography estimator108, which takes the vehicle information into account when estimatingdemography.

The example driving traffic analyzer 126 determines whether there areadditional traffic indicators to be analyzed (block 1112). If there areadditional traffic indicators 602, 604 to be analyzed (block 1112),control returns to block 1104 to select another traffic indicator 602,604. If there are no additional traffic indicators 602, 604 to beanalyzed (block 1112), the example instructions 1100 of FIG. 11 end.

FIG. 12 is a block diagram of an example processor platform 1200 capableof executing the instructions of FIGS. 7, 8A, 8B, 9, 10, and/or 11 toimplement the system 100 of FIG. 1. The processor platform 1200 can be,for example, a server, a personal computer, a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™, or any other typeof computing device.

The processor platform 1200 of the illustrated example includes aprocessor 1212. The processor 1212 of the illustrated example ishardware. For example, the processor 1212 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1212 of the illustrated example includes a local memory1213 (e.g., a cache). The processor 1212 of the illustrated example isin communication with a main memory including a volatile memory 1214 anda non-volatile memory 1216 via a bus 1218. The volatile memory 1214 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1216 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1214,1216 is controlled by a memory controller.

The processor platform 1200 of the illustrated example also includes aninterface circuit 1220. The interface circuit 1220 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1222 are connectedto the interface circuit 1220. The input device(s) 1222 permit(s) a userto enter data and commands into the processor 1212. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1224 are also connected to the interfacecircuit 1220 of the illustrated example. The output devices 1224 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a light emitting diode (LED), a printer and/or speakers).The interface circuit 1220 of the illustrated example, thus, typicallyincludes a graphics driver card, a graphics driver chip or a graphicsdriver processor.

The interface circuit 1220 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1226 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1200 of the illustrated example also includes oneor more mass storage devices 1228 for storing software and/or data.Examples of such mass storage devices 1228 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 1232 of FIGS. 7, 8A, 8B, 9, 10, and/or 11 may bestored in the mass storage device 1228, in the volatile memory 1214, inthe non-volatile memory 1216, and/or on a removable tangible computerreadable storage medium such as a CD or DVD.

The above-disclosed example methods and apparatus reduce costsassociated with determining demography of geographic areas when comparedwith current methods. Example methods and apparatus may be used toprovide estimates of demographies in areas that cannot be sampleddirectly, and can estimate demography more quickly and less expensivelythan performing sampling or surveying. The demography estimated byexample methods and apparatus disclosed herein may be used to, forexample, identify emerging markets and/or a developing potential formarketing goods and services to an underserved demographic.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method, comprising: analyzing a first aerialimage of a first geographic area to detect a first plurality of objects;and estimating a demographic characteristic of the first geographic areabased on the first plurality of objects.
 2. A method as defined in claim1, further comprising classifying the first plurality of objects.
 3. Amethod as defined in claim 2, wherein classifications of the firstplurality of objects comprise at least one of a swimming pool, a firsttype of structure roof, a green space, a distance range to a referencelocation, a landmark, or a roadway width range.
 4. A method as definedin claim 1, further comprising analyzing a second aerial image of asecond geographic area to detect a second plurality of objects, whereinestimating the demographic characteristic of the first geographic areais based on associating sampled data for the second geographic area tothe second plurality of objects and comparing the second plurality ofobjects to the first plurality of objects.
 5. A method as defined inclaim 1, further comprising identifying a second geographic area bycomparing a first signature of the first aerial image and a secondsignature of the second aerial image.
 6. A method as defined in claim 5,wherein estimating the demographic characteristic of the first areacomprises imputing ground truth characteristics of the second area tothe first area.
 7. A method as defined in claim 1, further comprisingdetermining a density of the first plurality of objects, whereinestimating the demographic characteristic of the first geographic areais based on the density of the first plurality of objects.
 8. A methodas defined in claim 1, wherein the demographic characteristic compriseshousehold income, population density, population age, or a family size.9. A method as defined in claim 1, further comprising analyzing websearch terms for searches performed in the geographic area, thedemographic characteristic being based on the web search terms.
 10. Amethod as defined in claim 1, further comprising estimating drivingtraffic originating and terminating in the geographic area, thedemographic characteristic being based on the estimate of the drivingtraffic.
 11. An apparatus, comprising: an object detector to detect afirst plurality of objects in a first geographic area by analyzing afirst aerial image of the first geographic area; and a demographyestimator to estimate a demographic characteristic of the firstgeographic area based on the first plurality of objects.
 12. Anapparatus as defined in claim 11, further comprising an objectclassifier to classify the first plurality of objects by analyzingfeatures in the first aerial image corresponding to the first pluralityof objects.
 13. An apparatus as defined in claim 12, whereinclassifications of the first plurality of objects comprise at least twoof a swimming pool, a first type of structure roof, a green space, adistance range to a reference location, or a roadway width range.
 14. Anapparatus as defined in claim 11, wherein the object detector is toanalyze a second aerial image of a second geographic area to detect asecond plurality of objects, the demography estimator to estimate thedemographic characteristic for the first area based on associatingsampled data for the second geographic area to the second plurality ofobjects and comparing the second plurality of objects to the firstplurality of objects.
 15. An apparatus as defined in claim 11, whereinthe object detector comprises a signature generator to generate asignature of the first aerial image and a second aerial image, thesecond aerial image corresponding to a second geographic area differentthan the first geographic area, the object detector to identify thesecond geographic area by comparing a first signature of the firstaerial image and a second signature of the second aerial image.
 16. Anapparatus as defined in claim 11, further comprising a densitycalculator to determine a density of the first plurality of objects, thedemography estimator to estimate the demographic characteristic of thefirst geographic area based on the density of the first plurality ofobjects.
 17. An apparatus as defined in claim 11, wherein thedemographic characteristic comprises population income, populationdensity, population age, or a family size.
 18. An apparatus as definedin claim 11, further comprising a search analyzer to analyze web searchterms for searches performed in the geographic area, the demographyestimator to estimate the demographic characteristic based on the websearches.
 19. An apparatus as defined in claim 11, further comprising adriving traffic analyzer to estimate driving traffic originating andterminating in the geographic area, the demography estimator to estimatethe demographic characteristic based on the estimate of the drivingtraffic.
 20. A computer readable storage medium comprising computerreadable instructions which, when executed, cause a processor to atleast: analyze a first aerial image of a first geographic area to detecta first plurality of objects; and estimate a demographic characteristicof the first geographic area based on the first plurality of objects.21. A storage medium as defined in claim 20, wherein the instructionsare further to cause the processor to classify the first plurality ofobjects.
 22. A storage medium as defined in claim 21, whereinclassifications of the first plurality of objects comprise at least oneof a swimming pool, a first type of structure roof, a green space, adistance range to a reference location, a landmark, or a roadway widthrange.
 23. A storage medium as defined in claim 20, wherein theinstructions are further to cause the processor to analyze a secondaerial image of a second geographic area to detect a second plurality ofobjects, wherein estimating the demographic characteristic of the firstgeographic area is based on associating sampled data for the secondgeographic area to the second plurality of objects and comparing thesecond plurality of objects to the first plurality of objects.
 24. Astorage medium as defined in claim 20, wherein the instructions arefurther to cause the processor to identify a second geographic area bycomparing a first signature of the first aerial image and a secondsignature of the second aerial image.
 25. A storage medium as defined inclaim 24, wherein the instructions are further to cause the processor toestimate the demographic characteristic of the first area by imputingground truth characteristics of the second area to the first area.
 26. Astorage medium as defined in claim 20, wherein the instructions arefurther to cause the processor to determine a density of the firstplurality of objects, wherein estimating the demographic characteristicof the first geographic area is based on the density of the firstplurality of objects.